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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF <t>decomposition</t> strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.
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Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF decomposition strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.

Journal: Advanced Science

Article Title: Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES

doi: 10.1002/advs.202508346

Figure Lengend Snippet: Overview of EDGES. a) The inputs of EDGES consist of ST data and a reference scRNA‐seq data. EDGES partitions the ST and scRNA‐seq data into X 1 , X 2 , and X 3 according to the shared genes and constructs, a cell–cell proximity graph based on the spatial coordinates. b) The optimization problem of EDGES. EDGES employs a spatially constrained NMF decomposition strategy to obtain joint low‐dimensional representations for ST and scRNA‐seq data. c) The outputs of EDGES include a denoised ST gene expression profile for measured genes and the predicted expressions for undetected genes.

Article Snippet: Wu , and D. Sun , “ Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES .” Adv.

Techniques: Construct, Gene Expression